45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
#
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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from typing import Any
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import torch
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from vllm_ascend.quantization.methods.base import AscendLinearScheme
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class AscendW8A8Linear310pScheme(AscendLinearScheme):
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def get_weight(
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self,
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype = torch.float16,
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) -> dict[str, Any]:
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return {"weight": torch.empty(output_size, input_size, dtype=torch.int8)}
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def get_pertensor_param(self, params_dtype: torch.dtype, **kwargs: Any) -> dict[str, Any]:
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return {
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"input_scale": torch.empty(1, dtype=params_dtype),
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"input_offset": torch.empty(1, dtype=torch.int8),
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}
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def get_perchannel_param(self, output_size: int, params_dtype: torch.dtype) -> dict[str, Any]:
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return {
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"quant_bias": torch.empty(output_size, dtype=torch.int32),
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"deq_scale": torch.empty(output_size, dtype=torch.int64),
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}
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